The Complexity of Approximately Solving Influence Diagrams

Influence diagrams allow for intuitive and yet precise description of complex situations involving decision making under uncertainty. Unfortunately, most of the problems described by influence diagrams are hard to solve. In this paper we discuss the complexity of approximately solving influence diagrams. We do not assume no-forgetting or regularity, which makes the class of problems we address very broad. Remarkably, we show that when both the treewidth and the cardinality of the variables are bounded the problem admits a fully polynomial-time approximation scheme.

Remembering forwards: or how to live together in the future with divided memories

Memory is thought to be about the past. The past is a problem in conflict transformation. This lecture suggests memory can also be about the future. It introduces the notion of remembering forwards, which is contrasted with remembering backwards. The distinction between these two forms of remembering defines the burden of memory in post-conflict societies generally and specifically in Ireland. In societies emerging out of conflict, where divided memories in part constituted the conflict, social memory privileges remembering backward. Collective and personal memories elide within social memory to perpetuate divided group identities and contested personal narratives. Above all, social memory works to arbitrate the future, by predisposing an extreme memory culture that locks people into the past. Forgetting the past is impossible and undesirable. What is needed in societies emerging out of conflict is to be released from the hold that oppressive and haunting memories have over people. This lecture will suggest that this is found in the idea of remembering forwards. This is not the same as forgetting. It is remembering to cease to remember oppressive and haunting memories. It does not involve non-remembrance but active remembering: remembering to cease to remember the past. While the past lives in us always, remembering forwards assists us in not living in the past. Remembering forwards thus allows us to live in tolerance in the future despite the reality that divided memories endure and live on. The lecture further argues that these enduring divided memories need to be reimagined by the application of truth, tolerance, togetherness and trajectory. The lecture suggests that it is through remembering forwards with truth, tolerance, togetherness and trajectory that people in post-conflict societies can inherit the future despite their divided pasts and live in tolerance in the midst of contested memories.

Random Forest Based Approach for Concept-Drift Handling

Algorithms for concept drift handling are important for various applications including video analysis and smart grids. In this paper we present decision tree ensemble classication method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with îriginal random forest with incorporated replace-the-looser forgetting andother state-of-the-art concept-drift classiers like AWE2.